Q-omics provides the consensus-scored SERF2 profile across patient tissues and cancer cell-line models. SERF2 expression is associated with patient survival in 22 of 34 cancer types, with the highest sampling consensus in ACC. Among the 18 cancer types available for tumor–normal comparison, SERF2 is differentially expressed in 11, with the highest sampling consensus in LIHC. Additionally, SERF2 RNA expression shows 19,801 significant gene co-expression associations, with the highest sampling consensus in THYM. Together, these results highlight ACC, LIHC, and THYM as cancer lineages where SERF2 shows reproducible signals across survival, tumor–normal expression, and patient cross-omics analyses.
Every result is evaluated using two consensus scores. Sampling consensus measures how consistently a finding is reproduced within a cancer lineage across different conditions. Lineage consensus measures how broadly the result is shared across cancer types, distinguishing pan-cancer signals from lineage-specific patterns.
Premium analyses for SERF2 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes SERF2 survival associations across molecular data types. SERF2 RNA expression shows survival associations in the most cancer types (22), followed by mutation status (2) and mass-spec protein abundance (4). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible SERF2 RNA expression–survival associations across cancer types. High SERF2 expression shows unfavorable associations in ACC, HNSC, UCS, LIHC and LGG, but favorable associations in UCEC. The ACC Kaplan–Meier curve shows clear separation, with the high-expression group declining faster, consistent with the unfavorable association (log-rank p < 0.001). Together, the overview and detailed table identify ACC as the clearest survival context for SERF2 RNA expression.
This table summarizes SERF2 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 11, while mass-spec protein shows differences in 7. The strongest signals are observed in LIHC for RNA and LUAD for protein.
This table ranks reproducible tumor–normal expression differences for SERF2. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. SERF2 shows higher tumor expression in LIHC, KIRC, UCEC, BRCA, CHOL and HNSC. The LIHC box plot shows higher SERF2 RNA expression in tumor versus normal tissue (log2 FC = +0.984, t-test p < 0.001).
This table shows molecular features associated with SERF2 in patient tissues and cancer cell lines. In patient samples, SERF2 shows the broadest associations at the RNA and protein expression levels, with THYM recurring as the lineage with the largest associated feature set. In cancer cell lines, SERF2 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in UPPER_AERODIGESTIVE_TRACT, while CRISPR and shRNA rows add functional-dependency signals in SOFT_TISSUE and BREAST.
